Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes

Abstract

Deep learning-based informative band selection methods on hyperspectral images (HSIs) have recently gained intense attention to eliminate spectral correlation and redundancies. However, existing deep learning-based methods either need additional postprocessing strategies to select the descriptive bands or optimize the model indirectly due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network, named Dropout concrete autoencoder (CAE), is inspired by advances in the CAE and Dropout feature ranking (Dropout FR) strategy. Unlike traditional deep learning-based methods; the Dropout CAE is trained directly given the required band subset, eliminating the need for further postprocessing. The experimental results in four HSI scenes show that the Dropout CAE achieves substantial and effective performance levels that outperform competing methods.